Advanced AI discovers a treasure trove of gravitational lenses - Space.com

However, astronomers had only about a hundred good gravitational lenses to use. .

Now, a team led by Kim-Vy Tran, an astronomer at ASTRO 3D (the ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions) and the University of New South Wales in Australia, employed a machine-learning algorithm called a convolutional neural network to search for gravitational lenses in images taken by the Dark Energy Camera (DECam) on the Victor M.

The algorithm, developed by Colin Jacobs of the Swinburne University of Technology in Australia, sifted through tens of millions of galaxy images to select a sample of 5,000 candidate gravitational lenses that are not immediately obvious to the human eye.

'These lenses are very small, so if you have fuzzy images, you're not going to really be able to detect them," Tran said in a statement (opens in new tab)M

Keck Observatory in Hawaii and the Very Large Telescope in Chile to follow up on 77 of the 5,000 candidate lenses.

The lenses are typically at higher redshifts than most previously known lenses, meaning that astronomers can see deeper into the universe with them.

The algorithm's 88% success rate in finding new lenses means there are now potentially thousands of new lenses for astronomers to choose from, though Tran said the team's aim was more modest. .

The average redshift of the lenses is 0.58, which corresponds to a distance of about 5 billion light-years, whereas the faraway objects that are being magnified by the lenses are typically at redshifts of about 1.92, meaning their light set out about 10 billion years ago.

The lenses therefore provide promising targets for follow-up with observatories such as the Hubble Space Telescope and the James Webb Space Telescope!